By Mainframe environments are under pressure to absorb a fast-moving open source ecosystem while keeping pace with AI tooling, observability standards, and new distribution demands. The gap between what the broader cloud native world ships and what actually runs sustainably on mainframe operating systems is widening. Without a cohesive community strategy, enterprise mainframe platforms risk falling behind on the technologies that the rest of the stack takes for granted.
In this interview on TFiR, John Mertic, Director of Program Management at the Linux Foundation, covers the Open Mainframe Project’s roadmap for observability, open source portability, AI integration, and cross-community collaboration.
Guest: John Mertic, Director of Program Management at The Linux Foundation / Open Mainframe Project
Show: Let’s Talk
LinkedIn: John Mertic
Watch the discussion on YouTube
Here is what every platform engineer and enterprise architect working with mainframe needs to know.
Technical Deep Dive
Q: Is mainframe a first-class citizen in enterprise computing today, and what does it take to keep it that way?
John Mertic, Director of Program Management at the Linux Foundation, says the Open Mainframe Project is focused on answering exactly that question: what technologies and infrastructure are needed to ensure mainframe stays at the same tier as other enterprise platforms. The work is not defensive; it is forward-looking, targeting the same capabilities that cloud native and distributed environments are building right now. That means observability, open source sustainability, and AI are all active workstreams, not future considerations.
“The project is looking ahead and continuing to ask what technologies and capabilities are needed to ensure that the mainframe remains a first-class citizen in the enterprise computing landscape.”
John Mertic, Director of Program Management, Linux Foundation
Q: Why is observability becoming a priority for mainframe environments?
Mertic identifies observability as one of the key crossover technology areas driving active work within the Open Mainframe Project community. As enterprises standardize on observability tooling across their stacks, mainframe cannot be the blind spot. The community is investing in this area to ensure mainframe workloads are visible within the same monitoring and telemetry frameworks used elsewhere in the enterprise.
“We’re seeing observability emerge as a key focus area, with a lot of work being driven in that direction.”
John Mertic, Director of Program Management, Linux Foundation
Q: How is the mainframe community building a sustainable open source strategy?
Mertic describes a push for a cohesive and aligned strategy that covers three connected problems: porting open source to the mainframe platform, maintaining those ports over time, and distributing packages to mainframe operating systems. The goal is a repeatable, community-driven process rather than one-off porting efforts that do not get sustained. This addresses one of the most persistent gaps between the mainframe ecosystem and the broader open source world.
“There is certainly work underway to develop a cohesive and aligned strategy for supporting the porting of more open-source software, establishing a sustainable approach to maintaining open source on the platform, and enabling the distribution of these packages to mainframe operating systems.”
John Mertic, Director of Program Management, Linux Foundation
Q: Where does AI fit into the mainframe open source roadmap?
Mertic points to agentic AI and frameworks like PyTorch as specific areas where the community is exploring what is applicable to mainframe environments. The question is not whether AI matters but how tools already gaining traction in the broader open source ecosystem can be partnered with and applied on mainframe. This exploration is happening across vertical-focused foundations, including those in motion picture, energy, and other industries, as well as horizontal collaboration projects.
“There’s this thing called AI that everyone is excited and enthusiastic about, and we’re starting to explore the possibilities, potential areas of collaboration, and ways to leverage existing tools from other open-source areas, such as agentic AI and PyTorch, and apply them here through partnerships.”
John Mertic, Director of Program Management, Linux Foundation
Q: What does expanding access to open source initiatives mean for the mainframe community going forward?
Mertic frames the Open Mainframe Project’s current moment as an enablement platform: the work being done now on open source sustainability and AI exploration opens the next tier of questions about what the community can tackle together. As vertical and horizontal foundations converge on common questions about how new technologies fit their environments, the project becomes the structural home for that collaboration rather than a point solution for a single problem.
“This will be a huge enabler because it helps establish this as a hub for driving collaboration. More broadly, it opens the door to the next level of discussions about which emerging technologies the community can collaborate on.”
John Mertic, Director of Program Management, Linux Foundation
Resources & Documentation
- Open Mainframe Project, Linux Foundation-hosted community advancing mainframe open source adoption and sustainability
- PyTorch, open source machine learning framework referenced as a candidate for mainframe AI collaboration
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👇 Click to Read Full Raw Transcript
Swapnil Bhartiya: Now this was something that you and I talked for a long time. Finally it is happening, it has happened. What is next for the project as you look to expand access to more open source initiatives?
John Mertic: You know, I think the project’s looking down the road here and just continuing to answer the question of, or ask the question rather of what are the technologies, what are the things in place that are really needed to, to have Mainframe continue to be that first class citizen within an enterprise computing realm. And there’s certainly a lot of other crossover technology things out there. We’re seeing observability being a key thing that’s being driven. And so we’re seeing a lot of work that’s happening that way. There certainly is work on how can we have a cohesive and aligned strategy on helping port more open source and having a sustained way of maintaining open source that, that works on the platform, some more broad open source, along with ways to do distribution of these packages out to mainframe operating systems. But then I think just like everybody else you talk to, there’s this thing called AI out there that everyone’s lots of excitement and lots of enthusiasm and I think we’re starting to see in this realm here of what are the possibilities and what are the collaboration areas, what is the ways that there’s already tooling in various other open source areas such as what we’re seeing in agentic AI and PyTorch and others that really of how could those sort of be applied here and partnered with here. And I think that’s, you see that if you talk with any of our EDs or anybody within a lot of our vertical oriented four app foundations, motion picture, energy, whatever, you know, our horizontal ones are very big intersection points of like, hey, how do these technologies fit within this ecosystem? How do they fit with this in the environment, how they fit here? And so I think that’s the area that we’re seeing the, the next level of exploration beginning to happen, of what does that look like, where are the opportunities there? And you know, we’re already hearing, we’re already seeing this percolate all over the place. So this, this, this I think will be a huge enablement thing because this helps solidify this as a home for driving this collaboration. But I think as the bigger picture here, it begins to open the next level of questions of what are the next technologies that this community can look to collaborate on.
